PROJECT SUMMARY Identifying genetic variants associated with complex diseases via genome-wide SNP and whole genome sequencing (WGS) studies has outpaced our ability to translate these findings into actionable biologic and clinical insights. We need to use in silico methods that integrate multiple layers of data, including transcriptomic, epigenetic, social, and environmental, to focus experimental validation on the most impactful targets. Asthma-related deaths are 4-fold higher in minority children than white children. Moreover, minority children with asthma have markedly decreased drug response to albuterol, a bronchodilator rescue medication that is the most commonly prescribed asthma medication in the world, and to glucocorticoids, anti-inflammatory medications that decrease symptoms and exacerbations. Our goal is to understand the biological basis of differential drug response that leads to observed racial/ethnic asthma disparities. In this proposal, we use two cloud-based apps we developed to identify functional biologic mechanisms of genes that are associated with racial/ethnic variation in asthma therapies. Specifically, our apps 1) provide gene-centric WGS association findings in the context of integrated multi-tissue omic results, and 2) reprioritize WGS association results using machine-learned tissue-specific networks constructed from gene expression, known protein-protein interactions, and established functional pathways. Our results will increase knowledge about the biological role of genes associated with asthma therapy and facilitate design of experiments to understand their function.